1. Technical Field
The present disclosure relates to smart grid energy distribution automation networks. More particularly, disclosure describes a holistic optimization approach for the assessment of the impact of multiple distribution automation failures after a major storm.
2. Discussion of the Related Art
Serious storms have historically caused multiple energy grid failures that are often localized in an area affected by the storm. In a distributed automation power distribution environment, or smart grid network, it may be possible to quickly and automatically recover from such multiple failures if the correct grid upgrades and storm hardening investments have been made. Given limited budgets and other constraints, however, it is necessary to select a limited number of grid upgrades and storm hardening projects to be implemented to maximize the likelihood of a quick and automated recovery.
There is a need for a survivability metric to be used in choosing such grid upgrades. Traditionally, the reliability of power systems has been quantified using average metrics, such as System Average Interruption Duration Index (“SAIDI”). Some of the United States public service commissions use SAIDI to assess utilities' compliance with the commission rules. SAIDI was developed to track manual restoration times, and according to Standard 166-1998, the median value for North American utilities is roughly one and a half hours.
In smart grid networks, power failure and restoration events will have a finer level of granularity, due to the deployment of reclosers, which isolate faulty sections, and demand side management system activities, such as distributed generators and demand response application systems. Therefore, there is a need to extend the SAIDI metric, and to develop new models and tools for the accurate computation of customer interruption indexes after power failure events occur, even if the occurrence of such events is rare.
Engineering of distributed automation power grids requires a careful assessment of an expected multiple-failure model. Power engineers presently do not have modeling capabilities to assess the joint impact of multiple failure identification, isolation and restoration based on the distributed automation power grid survivability. There is a need for the ability to assess the impact of multiple failures on distribution automation performance to be able to optimize the investment in the power grid survivability infrastructure.
There is a need to make a holistic assessment of system survivability after a major distribution automation failure event that causes multiple failures. The assessment should take into account the historic storm record as well as the topology and geography of the grid.